OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Perception and Immersion in Pre-Captured Environments through Learning-Based Eye Height Adaptation
Venue: arXiv cs.CV 2023
Year: 2023

Pre-captured immersive environments using omnidirectional cameras provide a wide range of virtual reality applications. Previous research has shown that manipulating the eye height in egocentric virtual environments can significantly affect distance perception and immersion. However, the influence of eye height in pre-captured real environments has received less attention due to the difficulty of altering the perspective after finishing the capture process. To explore this influence, we first propose a pilot study that captures real environments with multiple eye heights and asks participants to judge the egocentric distances and immersion. If a significant influence is confirmed, an effective image-based approach to adapt pre-captured real-world environments to the user's eye height would be desirable. Motivated by the study, we propose a learning-based approach for synthesizing novel views for omnidirectional images with altered eye heights. This approach employs a multitask architecture that learns depth and semantic segmentation in two formats, and generates high-quality depth and semantic segmentation to facilitate the inpainting stage. With the improved omnidirectional-aware layered depth image, our approach synthesizes natural and realistic visuals for eye height adaptation. Quantitative and qualitative evaluation shows favorable results against state-of-the-art methods, and an extensive user study verifies improved perception and immersion for pre-captured real-world environments.
Exploring Gender-Based Toxic Speech on Twitter in Context of the #MeToo movement: A Mixed Methods Approach
Venue: arXiv cs.SI 2023
Year: 2023

The #MeToo movement has catalyzed widespread public discourse surrounding sexual harassment and assault, empowering survivors to share their stories and holding perpetrators accountable. While the movement has had a substantial and largely positive influence, this study aims to examine the potential negative consequences in the form of increased hostility against women and men on the social media platform Twitter. By analyzing tweets shared between October 2017 and January 2020 by more than 47.1k individuals who had either disclosed their own sexual abuse experiences on Twitter or engaged in discussions about the movement, we identify the overall increase in gender-based hostility towards both women and men since the start of the movement. We also monitor 16 pivotal real-life events that shaped the #MeToo movement to identify how these events may have amplified negative discussions targeting the opposite gender on Twitter. Furthermore, we conduct a thematic content analysis of a subset of gender-based hostile tweets, which helps us identify recurring themes and underlying motivations driving the expressions of anger and resentment from both men and women concerning the #MeToo movement. This study highlights the need for a nuanced understanding of the impact of social movements on online discourse and underscores the importance of addressing gender-based hostility in the digital sphere.
Benchmarking Data Efficiency and Computational Efficiency of Temporal Action Localization Models
Venue: arXiv cs.CV 2023
Year: 2023

In temporal action localization, given an input video, the goal is to predict which actions it contains, where they begin, and where they end. Training and testing current state-of-the-art deep learning models requires access to large amounts of data and computational power. However, gathering such data is challenging and computational resources might be limited. This work explores and measures how current deep temporal action localization models perform in settings constrained by the amount of data or computational power. We measure data efficiency by training each model on a subset of the training set. We find that TemporalMaxer outperforms other models in data-limited settings. Furthermore, we recommend TriDet when training time is limited. To test the efficiency of the models during inference, we pass videos of different lengths through each model. We find that TemporalMaxer requires the least computational resources, likely due to its simple architecture.
Hybrid Genetic Algorithm and Hill Climbing Optimization for the Neural Network
Venue: arXiv cs.NE 2023
Year: 2023

In this paper, we propose a hybrid model combining genetic algorithm and hill climbing algorithm for optimizing Convolutional Neural Networks (CNNs) on the CIFAR-100 dataset. The proposed model utilizes a population of chromosomes that represent the hyperparameters of the CNN model. The genetic algorithm is used for selecting and breeding the fittest chromosomes to generate new offspring. The hill climbing algorithm is then applied to the offspring to further optimize their hyperparameters. The mutation operation is introduced to diversify the population and to prevent the algorithm from getting stuck in local optima. The Genetic Algorithm is used for global search and exploration of the search space, while Hill Climbing is used for local optimization of promising solutions. The objective function is the accuracy of the trained neural network on the CIFAR-100 test set. The performance of the hybrid model is evaluated by comparing it with the standard genetic algorithm and hill-climbing algorithm. The experimental results demonstrate that the proposed hybrid model achieves better accuracy with fewer generations compared to the standard algorithms. Therefore, the proposed hybrid model can be a promising approach for optimizing CNN models on large datasets.
DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT
Venue: arXiv cs.LG 2023
Year: 2023

Federated learning (FL) aims to collaboratively train a global model while ensuring client data privacy. However, FL faces challenges from the non-IID data distribution among clients. Clustered FL (CFL) has emerged as a promising solution, but most existing CFL frameworks adopt synchronous frameworks lacking asynchrony. An asynchronous CFL framework called SDAGFL based on directed acyclic graph distributed ledger techniques (DAG-DLT) was proposed, but its complete decentralization leads to high communication and storage costs. We propose DAG-ACFL, an asynchronous clustered FL framework based on directed acyclic graph distributed ledger techniques (DAG-DLT). We first detail the components of DAG-ACFL. A tip selection algorithm based on the cosine similarity of model parameters is then designed to aggregate models from clients with similar distributions. An adaptive tip selection algorithm leveraging change-point detection dynamically determines the number of selected tips. We evaluate the clustering and training performance of DAG-ACFL on multiple datasets and analyze its communication and storage costs. Experiments show the superiority of DAG-ACFL in asynchronous clustered FL. By combining DAG-DLT with clustered FL, DAG-ACFL realizes robust, decentralized and private model training with efficient performance.
How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection
Venue: arXiv cs.CV 2023
Year: 2023

Object detection (OD) in computer vision has made significant progress in recent years, transitioning from closed-set labels to open-vocabulary detection (OVD) based on large-scale vision-language pre-training (VLP). However, current evaluation methods and datasets are limited to testing generalization over object types and referral expressions, which do not provide a systematic, fine-grained, and accurate benchmark of OVD models' abilities. In this paper, we propose a new benchmark named OVDEval, which includes 9 sub-tasks and introduces evaluations on commonsense knowledge, attribute understanding, position understanding, object relation comprehension, and more. The dataset is meticulously created to provide hard negatives that challenge models' true understanding of visual and linguistic input. Additionally, we identify a problem with the popular Average Precision (AP) metric when benchmarking models on these fine-grained label datasets and propose a new metric called Non-Maximum Suppression Average Precision (NMS-AP) to address this issue. Extensive experimental results show that existing top OVD models all fail on the new tasks except for simple object types, demonstrating the value of the proposed dataset in pinpointing the weakness of current OVD models and guiding future research. Furthermore, the proposed NMS-AP metric is verified by experiments to provide a much more truthful evaluation of OVD models, whereas traditional AP metrics yield deceptive results. Data is available at \url{https://github.com/om-ai-lab/OVDEval}
MMBAttn: Max-Mean and Bit-wise Attention for CTR Prediction
Venue: arXiv cs.IR 2023
Year: 2023

With the increasing complexity and scale of click-through rate (CTR) prediction tasks in online advertising and recommendation systems, accurately estimating the importance of features has become a critical aspect of developing effective models. In this paper, we propose an attention-based approach that leverages max and mean pooling operations, along with a bit-wise attention mechanism, to enhance feature importance estimation in CTR prediction. Traditionally, pooling operations such as max and mean pooling have been widely used to extract relevant information from features. However, these operations can lead to information loss and hinder the accurate determination of feature importance. To address this challenge, we propose a novel attention architecture that utilizes a bit-based attention structure that emphasizes the relationships between all bits in features, together with maximum and mean pooling. By considering the fine-grained interactions at the bit level, our method aims to capture intricate patterns and dependencies that might be overlooked by traditional pooling operations. To examine the effectiveness of the proposed method, experiments have been conducted on three public datasets. The experiments demonstrated that the proposed method significantly improves the performance of the base models to achieve state-of-the-art results.
Journey to the Center of the Knowledge Neurons: Discoveries of Language-Independent Knowledge Neurons and Degenerate Knowledge Neurons
Venue: arXiv cs.CL 2023
Year: 2023

Pre-trained language models (PLMs) contain vast amounts of factual knowledge, but how the knowledge is stored in the parameters remains unclear. This paper delves into the complex task of understanding how factual knowledge is stored in multilingual PLMs, and introduces the Architecture-adapted Multilingual Integrated Gradients method, which successfully localizes knowledge neurons more precisely compared to current methods, and is more universal across various architectures and languages. Moreover, we conduct an in-depth exploration of knowledge neurons, leading to the following two important discoveries: (1) The discovery of Language-Independent Knowledge Neurons, which store factual knowledge in a form that transcends language. We design cross-lingual knowledge editing experiments, demonstrating that the PLMs can accomplish this task based on language-independent neurons; (2) The discovery of Degenerate Knowledge Neurons, a novel type of neuron showing that different knowledge neurons can store the same fact. Its property of functional overlap endows the PLMs with a robust mastery of factual knowledge. We design fact-checking experiments, proving that the degenerate knowledge neurons can help the PLMs to detect wrong facts. Experiments corroborate these findings, shedding light on the mechanisms of factual knowledge storage in multilingual PLMs, and contribute valuable insights to the field. The code is available at https://github.com/heng840/AMIG.
Knowledge-Based Version Incompatibility Detection for Deep Learning
Venue: arXiv cs.SE 2023
Year: 2023

Version incompatibility issues are rampant when reusing or reproducing deep learning models and applications. Existing techniques are limited to library dependency specifications declared in PyPI. Therefore, these techniques cannot detect version issues due to undocumented version constraints or issues involving hardware drivers or OS. To address this challenge, we propose to leverage the abundant discussions of DL version issues from Stack Overflow to facilitate version incompatibility detection. We reformulate the problem of knowledge extraction as a Question-Answering (QA) problem and use a pre-trained QA model to extract version compatibility knowledge from online discussions. The extracted knowledge is further consolidated into a weighted knowledge graph to detect potential version incompatibilities when reusing a DL project. Our evaluation results show that (1) our approach can accurately extract version knowledge with 84% accuracy, and (2) our approach can accurately identify 65% of known version issues in 10 popular DL projects with a high precision (92%), while two state-of-the-art approaches can only detect 29% and 6% of these issues with 33% and 17% precision respectively.
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-Diversity
Venue: arXiv cs.LG 2023
Year: 2023

Quality-Diversity is a branch of stochastic optimization that is often applied to problems from the Reinforcement Learning and control domains in order to construct repertoires of well-performing policies/skills that exhibit diversity with respect to a behavior space. Such archives are usually composed of a finite number of reactive agents which are each associated to a unique behavior descriptor, and instantiating behavior descriptors outside of that coarsely discretized space is not straight-forward. While a few recent works suggest solutions to that issue, the trajectory that is generated is not easily customizable beyond the specification of a target behavior descriptor. We propose to jointly solve those problems in environments where semantic information about static scene elements is available by leveraging a Large Language Model to augment the repertoire with natural language descriptions of trajectories, and training a policy conditioned on those descriptions. Thus, our method allows a user to not only specify an arbitrary target behavior descriptor, but also provide the model with a high-level textual prompt to shape the generated trajectory. We also propose an LLM-based approach to evaluating the performance of such generative agents. Furthermore, we develop a benchmark based on simulated robot navigation in a 2d maze that we use for experimental validation.
Construction Grammar and Language Models
Venue: arXiv cs.CL 2023
Year: 2023

Recent progress in deep learning and natural language processing has given rise to powerful models that are primarily trained on a cloze-like task and show some evidence of having access to substantial linguistic information, including some constructional knowledge. This groundbreaking discovery presents an exciting opportunity for a synergistic relationship between computational methods and Construction Grammar research. In this chapter, we explore three distinct approaches to the interplay between computational methods and Construction Grammar: (i) computational methods for text analysis, (ii) computational Construction Grammar, and (iii) deep learning models, with a particular focus on language models. We touch upon the first two approaches as a contextual foundation for the use of computational methods before providing an accessible, yet comprehensive overview of deep learning models, which also addresses reservations construction grammarians may have. Additionally, we delve into experiments that explore the emergence of constructionally relevant information within these models while also examining the aspects of Construction Grammar that may pose challenges for these models. This chapter aims to foster collaboration between researchers in the fields of natural language processing and Construction Grammar. By doing so, we hope to pave the way for new insights and advancements in both these fields.
COCO: Testing Code Generation Systems via Concretized Instructions
Venue: arXiv cs.SE 2023
Year: 2023

Code generation systems have been extensively developed in recent years to generate source code based on natural language instructions. However, despite their advancements, these systems still face robustness issues where even slightly different instructions can result in significantly different code semantics. Robustness is critical for code generation systems, as it can have significant impacts on software development, software quality, and trust in the generated code. Although existing testing techniques for general text-to-text software can detect some robustness issues, they are limited in effectiveness due to ignoring the characteristics of code generation systems. In this work, we propose a novel technique COCO to test the robustness of code generation systems. It exploits the usage scenario of code generation systems to make the original programming instruction more concrete by incorporating features known to be contained in the original code. A robust system should maintain code semantics for the concretized instruction, and COCO detects robustness inconsistencies when it does not. We evaluated COCO on eight advanced code generation systems, including commercial tools such as Copilot and ChatGPT, using two widely-used datasets. Our results demonstrate the effectiveness of COCO in testing the robustness of code generation systems, outperforming two techniques adopted from general text-to-text software testing by 466.66% and 104.02%, respectively. Furthermore, concretized instructions generated by COCO can help reduce robustness inconsistencies by 18.35% to 53.91% through fine-tuning.
3D Face Alignment Through Fusion of Head Pose Information and Features
Venue: arXiv cs.CV 2023
Year: 2023

The ability of humans to infer head poses from face shapes, and vice versa, indicates a strong correlation between the two. Accordingly, recent studies on face alignment have employed head pose information to predict facial landmarks in computer vision tasks. In this study, we propose a novel method that employs head pose information to improve face alignment performance by fusing said information with the feature maps of a face alignment network, rather than simply using it to initialize facial landmarks. Furthermore, the proposed network structure performs robust face alignment through a dual-dimensional network using multidimensional features represented by 2D feature maps and a 3D heatmap. For effective dense face alignment, we also propose a prediction method for facial geometric landmarks through training based on knowledge distillation using predicted keypoints. We experimentally assessed the correlation between the predicted facial landmarks and head pose information, as well as variations in the accuracy of facial landmarks with respect to the quality of head pose information. In addition, we demonstrated the effectiveness of the proposed method through a competitive performance comparison with state-of-the-art methods on the AFLW2000-3D, AFLW, and BIWI datasets.
Enhancing Signal Space Diversity for SCMA Over Rayleigh Fading Channels
Venue: arXiv cs.IT 2023
Year: 2023

Sparse code multiple access (SCMA) is a promising technique for the enabling of massive connectivity in future machine-type communication networks, but it suffers from a limited diversity order which is a bottleneck for significant improvement of error performance. This paper aims for enhancing the signal space diversity of sparse code multiple access (SCMA) by introducing quadrature component delay to the transmitted codeword of a downlink SCMA system in Rayleigh fading channels. Such a system is called SSD-SCMA throughout this work. By looking into the average mutual information (AMI) and the pairwise error probability (PEP) of the proposed SSD-SCMA, we develop novel codebooks by maximizing the derived AMI lower bound and a modified minimum product distance (MMPD), respectively. The intrinsic asymptotic relationship between the AMI lower bound and proposed MMPD based codebook designs is revealed. Numerical results show significant error performance improvement in the both uncoded and coded SSD-SCMA systems.
Small Celestial Body Exploration with CubeSat Swarms
Venue: arXiv cs.RO 2023
Year: 2023

This work presents a large-scale simulation study investigating the deployment and operation of distributed swarms of CubeSats for interplanetary missions to small celestial bodies. Utilizing Taylor numerical integration and advanced collision detection techniques, we explore the potential of large CubeSat swarms in capturing gravity signals and reconstructing the internal mass distribution of a small celestial body while minimizing risks and Delta V budget. Our results offer insight into the applicability of this approach for future deep space exploration missions.
TFDNet: Time-Frequency Enhanced Decomposed Network for Long-term Time Series Forecasting
Venue: arXiv cs.LG 2023
Year: 2023

Long-term time series forecasting is a vital task and has a wide range of real applications. Recent methods focus on capturing the underlying patterns from one single domain (e.g. the time domain or the frequency domain), and have not taken a holistic view to process long-term time series from the time-frequency domains. In this paper, we propose a Time-Frequency Enhanced Decomposed Network (TFDNet) to capture both the long-term underlying patterns and temporal periodicity from the time-frequency domain. In TFDNet, we devise a multi-scale time-frequency enhanced encoder backbone and develop two separate trend and seasonal time-frequency blocks to capture the distinct patterns within the decomposed trend and seasonal components in multi-resolutions. Diverse kernel learning strategies of the kernel operations in time-frequency blocks have been explored, by investigating and incorporating the potential different channel-wise correlation patterns of multivariate time series. Experimental evaluation of eight datasets from five benchmark domains demonstrated that TFDNet is superior to state-of-the-art approaches in both effectiveness and efficiency.
SoTaNa: The Open-Source Software Development Assistant
Venue: arXiv cs.SE 2023
Year: 2023

Software development plays a crucial role in driving innovation and efficiency across modern societies. To meet the demands of this dynamic field, there is a growing need for an effective software development assistant. However, existing large language models represented by ChatGPT suffer from limited accessibility, including training data and model weights. Although other large open-source models like LLaMA have shown promise, they still struggle with understanding human intent. In this paper, we present SoTaNa, an open-source software development assistant. SoTaNa utilizes ChatGPT to generate high-quality instruction-based data for the domain of software engineering and employs a parameter-efficient fine-tuning approach to enhance the open-source foundation model, LLaMA. We evaluate the effectiveness of \our{} in answering Stack Overflow questions and demonstrate its capabilities. Additionally, we discuss its capabilities in code summarization and generation, as well as the impact of varying the volume of generated data on model performance. Notably, SoTaNa can run on a single GPU, making it accessible to a broader range of researchers. Our code, model weights, and data are public at \url{https://github.com/DeepSoftwareAnalytics/SoTaNa}.